1585 results about "Web technology" patented technology

The invention belongs to the technical field of image classification neural networks, and discloses a lightweight image classification neural network architecture system based on knowledge distillation. The invention provides a lightweight image classification neural network architecture system based on knowledge distillation. The lightweight image classification neural network architecture system comprises a data set preprocessing module, a teacher model preprocessing module, a subnet model construction module, a personalized training module, a real loss calculation module, a distillation loss calculation module, a total loss calculation module and a student model construction module. The invention provides a lightweight neural network architecture system based on knowledge distillation, and designs a brand new training mode by using the superiority of knowledge distillation for teacher model weight extraction: the knowledge distillation of teacher and student models is combined with a deep compression method; the system for constructing the lightweight network can be used for training various complex image classification neural networks so as to improve the network performance.

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

The invention relates to a power edge cloud cooperative management method based on a swan OS, and the method comprises the following steps: S1, deploying intelligent edge nodes, setting an embedded AI acceleration unit, operating the swan OS on all the intelligent edge nodes, and achieving the seamless cooperation of equipment through the distributed characteristics of the swan OS; and S2, realizing self-organizing connection of edge devices by adopting a Mesh network technology, designing a direct communication path between the edge devices, and realizing rapid processing of local problems. S3, aiming at the real-time requirement of a power system, optimizing an MQTT / CoAP protocol stack, improving the efficiency and reliability of message transmission, and allocating special network resources for different types of power applications by utilizing the 5G network slice characteristics, S4, constructing a security architecture based on a zero-trust principle, enhancing the equipment access control and data transmission security, and S5, establishing a security architecture based on a zero-trust principle. And identity authentication and data integrity protection are performed by using a block chain technology, so that data tampering and counterfeiting are prevented. According to the method, the local distributed decision-making capability and the global cooperation capability are effectively improved.

The invention relates to the technical field of wireless communication networks, in particular to a vehicle ad hoc network based on intelligent networked vehicle WiFi and a V2V interaction method, a vehicle motion state and network load data are acquired through a vehicle-mounted sensor, AP / STA modes are dynamically switched by combining a deep Q network (DQN) model and an epsilon-greedy strategy, and an adaptive topological structure is constructed. And expanding OLSR protocol message embedding path delay fields, calculating and optimizing multi-hop routing selection in combination with pheromone gradient, and realizing message priority classification and differential privacy protection through a federated learning model. And dynamically adjusting an AP / STA proportion threshold based on a particle swarm algorithm, and triggering node role reconfiguration to balance network load. According to the method, the existing WiFi hardware is reused, the deployment cost is reduced, cross-layer routing optimization and a distributed decision mechanism are combined, and the network real-time performance, the expansibility and the group cooperation capability in a dynamic scene are improved.

The invention discloses a self-adaptive load balancing ring main unit, relates to the technical field of power distribution network protection, and solves the problems that the load balancing strategy of the existing ring main unit is not flexible enough and the monitoring and early warning capabilities are insufficient. The system comprises a data acquisition module, a data processing and analysis module, a load balance control module, a monitoring and early warning module and an expansion and compatibility module. Load distribution is intelligently optimized through real-time data and edge calculation; pID control and fuzzy logic are integrated, parameters are dynamically adjusted, and efficient operation of a power system is ensured; a non-dominated sorting genetic algorithm and an ant colony optimization algorithm are applied to balance energy consumption and stability on the basis of meeting load balance, and an optimal load scheme is generated; safety is improved through closed-loop control and an early warning system, network resource management is enhanced through the software defined network technology, and it is ensured that the system is flexible and extensible; according to the invention, the intelligent load balancing capability of the existing ring main unit is greatly improved, and the operation efficiency and stability are enhanced.

The invention relates to the technical field of communication networks, and discloses a service channel time slot allocation method, which comprises the following steps of: 1, acquiring network data in real time from network equipment, the network data comprising bandwidth utilization rate, delay, packet loss rate and service flow type; 2, predicting future network flow fluctuation through a long-short-term memory network model based on historical flow data and real-time network data; and step 3, dynamically adjusting the allocation of time slot resources according to the network flow fluctuation prediction result, and preferentially allocating the time slot resources for high-priority services. According to the method, the dynamic adjustment of the time slot resources is realized by combining the flow prediction of the LSTM model and the Q-learning reinforcement learning algorithm, so that the system can flexibly allocate the resources according to the actual network load condition, the resource waste is reduced, the utilization efficiency of the bandwidth is improved, and the utilization rate of the overall network resources is remarkably improved especially under the condition of high network load.

The invention relates to the technical field of software defined networks, in particular to an SRv6-based SDN (Software Defined Network) data center load balancing method and system, and the method comprises the steps: obtaining node real-time load and SRv6 routing available resource information through an SDN controller, constructing a load balancing model, and recognizing an overload node and a congestion link; generating a flow scheduling path in combination with segmented routing characteristics; in combination with network state information, monitoring the load condition in real time when the node is unbalanced or the link packet loss rate is gt; when 5%, a load balancing adjustment mechanism is triggered, unbalance types are distinguished, a related flow set is determined, optimization is performed by using a DQN algorithm, and a flow scheduling strategy is generated and dynamically adjusted by taking throughput maximization and unbalance degree minimization as targets; and issuing the scheduling strategy to the data center network equipment, updating the flow table through the Netconf protocol, and redistributing the flow. Therefore, the problems of low load balancing efficiency, insufficient dynamic adaptability, low resource utilization rate and the like in the prior art are solved.

The invention discloses a high-dynamic mobile ad hoc network dynamic time slot allocation method and device, equipment and a storage medium, and belongs to the technical field of wireless communication and networks. The device is deployed between an upper-layer application module and a bottom-layer communication module of a communication node, and comprises a topology sensing module, a time frame construction module, an idle time slot distribution module, a GNSS module and an information interaction module. According to the method, neighbor nodes are determined among nodes through interaction information, and node topology change indexes and link states are calculated; the coordination node performs idle time slot allocation according to a request sent by the node in the control time slot, and dynamically adjusts a sensing period according to the current network topology change; and the node sends data in the information time slot according to the time slot allocation table, and carries out information interaction and topology perception according to the perception period. According to the method, the packet loss rate in the link unstable environment is effectively reduced, the data transmission reliability is higher, the method can adapt to link and topology dynamic changes, and the network throughput and the resource utilization rate are improved.

The present invention relates to the technical field of networks. Provided are a method and apparatus for adjusting the allocation of AI computing power hardware computing resources on the basis of the energy consumption of a base station. The method comprises: step S1, on the basis of a unit-time computing energy consumption function H and a computing time consumption function T of AI computing power hardware computing resources, obtaining training parameters during the training of an AI algorithm when the minimum value of computing energy consumption is taken, and setting the minimum value as predicted computing energy consumption of the AI computing power hardware computing resources during the training of the AI algorithm; S2, compiling statistics on energy consumption per unit time of each base station, and on the basis of the load balancing of each base station, estimating predicted reduced energy consumption of shutting down a base station; and S3, on the basis of the predicted computing energy consumption and the predicted reduced energy consumption, determining whether to perform the training of the AI algorithm or change the training parameters, wherein the training of the AI algorithm is used for computing a base station shutdown policy. By means of the method and apparatus in the present invention, the rational allocation and adjustment of hardware computing resources can be realized.

The invention discloses a regional power grid new energy consumption prediction and decision support system, which relates to the technical field of smart power grids and new energy, and comprises a data acquisition unit, a characteristic analysis unit, a prediction model construction unit, a decision generation unit and an interactive display unit, multi-source data such as new energy power generation, meteorological information and power grid operation are collected in real time through the data collection unit, key features are extracted through the feature analysis unit, a solid foundation is provided for prediction model construction, the prediction model constructed through the quantum fusion algorithm can accurately predict the new energy power generation capacity and the power grid load, and the prediction efficiency is improved. Meanwhile, the decision generation unit can quickly generate an optimal new energy consumption decision scheme based on a dynamic fuzzy neural network technology, effectively cope with power grid fluctuation and ensure stable operation of a power grid, and the functions jointly improve the utilization rate of new energy and the overall efficiency of the power grid.

The invention discloses a generative AI endogenous communication network architecture and a hierarchical collaborative scheduling method, and relates to the technical field of communication networks, the communication network architecture comprises an intention extraction layer, a mapping adaptation layer and a network execution layer, the intention extraction layer processes multi-modal data by using a generative AI model to obtain a semantic demand representation of a user, and the mapping adaptation layer carries out mapping on the semantic demand representation of the user; the mapping adaptation layer maps the semantic demand representation into a service quality index by using a generative AI model, maps a real-time network state of a heterogeneous network in the network execution layer into a network performance index, and generates a transmission scheduling strategy based on the service quality index and the network performance index, and the network execution layer works based on the transmission scheduling strategy, so that the network performance of the heterogeneous network is improved. End-to-end deterministic transmission scheduling of data packets of a service is implemented. According to the invention, efficient deterministic transmission scheduling of intelligent computing services in a 6G network can be realized.

The invention discloses a QoS guarantee method and system for a communication network, and relates to the technical field of communication networks, and the method comprises the steps: collecting and preprocessing network state data, and forming a standardized data set; dynamically classifying service types based on an improved random forest algorithm, and predicting a future QoS demand trend of each priority service in combination with an LSTM neural network; establishing a mapping model of QoS demands and resource parameters, converting predicted demands into allocable resource indexes, monitoring the resource utilization rate in real time, and setting an elastic reservation mechanism and conflict early warning; when early warning is triggered, selecting an optimal transmission link by adopting a multi-path collaborative algorithm, and implementing differentiated resource allocation according to service priorities; and a closed-loop feedback mechanism is triggered to dynamically adjust resource allocation by monitoring the deviation between the actual QoS and a predicted value in real time. The method has the advantages that through multi-dimensional perception, LSTM prediction, dynamic resource management and multi-path scheduling, QoS requirements of services with different priorities are accurately matched, and dynamic changes of the network are efficiently coped with.

The invention discloses a twin network performance prediction method and device based on a graph neural network and a storage medium, and belongs to the technical field of communication networks, and the method comprises the steps: carrying out the preprocessing of the obtained historical information of a physical communication network, and obtaining a dependency graph data set; initializing the state of each node in the dependency graph data set to obtain the initial hidden state of each node; the initial hidden state is used to train a pre-constructed network performance prediction model to obtain a trained network performance prediction model, and the network performance prediction model obtains iteratively updated hidden state information and adjusts model parameters through cooperative work of an attention mechanism, a message passing neural network and a gating circulation unit; and inputting the obtained real-time information of the physical communication network into the trained network performance prediction model to obtain a performance prediction result, thereby improving the stability, accuracy and efficiency of performance prediction of the digital twin communication network.

The invention relates to the technical field of communication networks, in particular to a real-time network flow prediction and resource optimization distribution method and system based on a space-time multi-mode generation model. Multi-modal feature extraction and fusion are carried out; model prediction is generated; reinforcement learning scheduling optimization; self-supervision and feedback closed loop are realized; executing resource allocation; the method has the advantages that multiple data sources such as spatio-temporal data, user behaviors and application types are fused through the multi-modal large model, future network traffic distribution is generated through the generation model, and the accuracy and timeliness of traffic prediction are improved. The network resources of different areas and time periods are intelligently scheduled through the reinforcement learning model, real-time and automatic resource allocation is realized, and the network quality and user experience of high-demand areas are ensured.

The invention discloses a front-end task scheduling and event processing method and device, equipment and a storage medium, and relates to the field of distributed systems and network technologies, and the front-end task scheduling and event processing method comprises the steps of capturing events triggered by all sub-elements; processing the captured events into standard task objects in a unified format; adding the standard task objects into a scheduling queue according to a priority sequence, and performing de-duplication processing and task merging processing on all the standard task objects in the scheduling queue to obtain a cleaned scheduling queue; all the standard task objects are sorted again according to the priority in the multi-task priority queue; according to the priorities of the standard task objects in the multi-task priority queue, dynamically adjusting the resource allocation of each standard task object in the multi-task priority queue in combination with the current load of the system; and executing the standard task object allocated with the resources by combining an asynchronous execution mode with a user feedback mechanism. According to the method, the problems of task conflict and resource competition in high-concurrency interaction can be effectively solved.

The invention provides an internal network threat intelligent analysis and prediction method and system based on a large language model, and relates to the technical field of network technology and security. According to the method, the long-range dependency processing capacity of the LLMs is utilized, global and long-range dependency analysis of the user behavior sequence is achieved, and the limitation of a traditional fixed session unit is broken through. Rich semantic features and context information are extracted from original log data through the deep semantic analysis ability of LLMs, and the feature expression ability is greatly improved. In combination with the reasoning capability of LLMs and a traditional anomaly detection algorithm, context-based deep semantic analysis is realized, and the recognition capability of complex threats is improved. In the aspects of real-time monitoring and response, the LLMs are introduced to assist in complex event processing and dynamic rule generation, and the intelligent level and efficiency are improved. Aiming at challenges of data management and analysis, an LLMs is utilized to enhance a data management strategy, a storage structure is optimized, and an intelligent data life cycle management and query interface is provided.

The invention provides an Internet of Vehicles vulnerability management method, system, device and medium based on a block chain, and relates to the technical field of vehicle networks, the method can comprehensively identify potential safety risks of vehicles through a dynamic and static combined vulnerability detection mechanism, and generates a structured vulnerability report; hash abstract chaining evidence storage is performed on key data of reports and subsequent repair links by using a block chain, so that the whole process of vulnerability discovery, analysis and repair is ensured not to be tampered and traceable, and the authenticity and credibility of data are improved; the integrity of the report is verified at the cloud end, and an AI analysis engine is combined to associate a CVE database and threat intelligence, so that intelligent generation and decision support of a repair scheme are realized; the hash abstract on the chain of the OTA patch is verified at the vehicle end, so that the credibility of the patch source and the integrity of the content are guaranteed; and finally, through feedback of an installation result and secondary uplink archiving, complete closed-loop management from vulnerability discovery to repair verification is formed.

The invention relates to the technical field of networks, in particular to a remote sensing image semantic segmentation network and method, and the method comprises the steps: constructing a remote sensing image semantic segmentation model based on context anchor point residual errors and multi-scale aggregation, the network structure of the model is mainly composed of a context anchor point residual block CARB, a multi-scale attention aggregation module MSAA and a mixed residual path MRP. In the model training stage, a composite loss function of a cross entropy loss function and a Dice coefficient loss function is used for assisting model training; original input images of different sizes are input into the network, through a series of feature extraction and classification of the network, a segmentation task with clear boundaries is finally completed on a target, a complete segmentation effect picture is output, the network can extract richer feature information, fine spatial features and deep semantic information of the images are effectively captured, and the image segmentation efficiency is improved. And the target identification capability is further enhanced, so that a better segmentation effect is realized in a complex environment.

The invention relates to the technical field of virtual local area networks, in particular to a communication system for dividing a virtual local area network (VLAN) into network cards based on a high-speed signal connection switch. The system comprises a network state monitoring module, a flow prediction module, a resource allocation decision module, a flow control execution module, a behavior pattern analysis module, a VLAN configuration adjustment module, a resource allocation optimization module and a network configuration adaptive adjustment module. According to the method, the software-defined network technology and the time sequence analysis are combined, the flow prediction is carried out by using the anomaly detection, the long-short-term memory network and the convolutional neural network, the bandwidth, the priority and the routing are adjusted through the optimization theory and reinforcement learning, the network flexibility is enhanced, and the strategy deployment efficiency is improved through the software-defined network technology; the recurrent neural network and the long short-term memory network deepen behavior recognition, the VLAN configuration is optimized by using the minimum cut maximum flow algorithm, the resource utilization rate is improved, the network adaptive adjustment adopts the genetic algorithm and simulated annealing, the routing is optimized, the QoS strategy is automatically adapted, and the adaptive capability is enhanced.

Techniques for computer science, data science, data analytics, computer software, algorithmic analysis, and networked technologies for sourcing, procurement, manufacturing, and supply chain management in small-to-medium manufacturing (“SMM”) industries that involve processes whereby implementing user input and extracted image and text from engineering drawings may be used to map manufacturing requirements to a subset of manufacturers via natural language agent programs. More specifically, natural language agent programs may be implemented to apply input and extracted image and text from engineering drawings to a large language model (“LLM”). An example method may include receiving a user input including data representing requirements to manufacture a physical structure, concatenating an engineering drawing summary data, a shoptype description, and estimated part size instruction data to combine the at least two of the engineering drawing summary data, the shoptype description, and the estimated part size instruction data to generate a subset (e.g., a list) of qualified manufacturers (e.g., SMMs).

The invention relates to the technical field of wireless communication networks, in particular to an eSIM global operator dynamic OTA switching system based on an AI strategy, and the system comprises an environment data collection module which collects the bandwidth, the packet loss rate, the network stability and the switching success rate at an equipment end, and generates local network state data; the behavior and tariff modeling module collects communication behavior and tariff information and generates user preference data; the AI strategy generation module outputs an OTA control instruction based on a graph neural network and an attention mechanism; the OTA strategy synchronization module issues and analyzes an instruction to generate an execution parameter packet; and the double-link switching execution module completes switching and rollback configuration. According to the invention, by fusing graph neural network strategy modeling, a terminal AI and OTA cooperation mechanism and a double-link redundancy switching process, intelligent, low-delay and high-reliability dynamic switching of an eSIM operator is realized.

The invention discloses a network security supervision system based on a network technology, and particularly relates to the technical field of data security, the network security supervision system comprises an intelligent detection terminal, an edge cooperation terminal, a cloud platform, a strategy management terminal, a management control terminal and a system database, the intelligent detection terminal is used for collecting network security data in real time and performing data preprocessing; the edge collaboration terminal is used for carrying out preliminary analysis on the integrated data and identifying common security threats; the cloud platform is used for carrying out multi-source data fusion, carrying out deep analysis and evaluating a network security situation; the strategy management terminal is used for formulating differentiated security strategies and dynamically adjusting the security strategies; the management control terminal is used for providing a visual operation interface for system management and decision making; and the system database is used for storing data for a long time and supporting a real-time retrieval function. By adopting a multi-layer distributed architecture and multi-source data fusion, the accuracy of security assessment is enhanced, and the efficiency of threat monitoring is improved.

The invention discloses a test method of a storage link, electronic equipment, a storage medium and a program product, and relates to the technical field of storage networks, and the test method comprises the following steps: initializing a plurality of controller nodes in a storage server to form one or more controller groups, and networking the controller groups to a storage cluster; configuring a switch partition matched with the current state of the port virtualization function of the controller group; verifying a routing path between the host and the storage cluster according to a first mapping relationship between the physical port and the virtual port, the current state and the switch partition; the routing path comprises a second mapping relation between the host and the physical port and a third mapping relation between the host and the virtual port, namely, the storage link between the host and the storage cluster can be tested. The technical problems that virtual port management is complex and manual configuration is prone to errors are solved, and the technical effects that the test coverage rate and efficiency are improved, and the system reliability and flexibility are enhanced are achieved.

The invention provides a network traffic scheduling optimization method based on deep learning. The method is applied to the technical field of communication networks, and comprises the following steps: S1, collecting flow data, link state, delay and packet loss rate indexes deployed at network nodes in real time, and constructing a sample database; s2, according to the sample database, constructing a traffic prediction model fusing a retrieval enhancement diffusion model and a mixed linear expert model to perform multi-modal traffic prediction; s3, constructing a state space of a reinforcement learning agent according to a real-time network state and the future traffic prediction result; s4, performing strategy optimization based on a multi-objective optimization mechanism; and S5, deploying the trained and optimized model to a network controller or an edge computing node to realize real-time sensing and dynamic scheduling control of network resources. According to the method, the traffic prediction precision is remarkably improved, the scheduling strategy is updated and optimized in time according to the network environment change, and the comprehensive performance of the network is improved.

The invention discloses an information communication network technology, a method and equipment. The information communication network comprises a service network and a bearing network, the service network is used for generating, organizing, managing and controlling services and applications on the information communication network, the bearing network is used for transmitting data packets generated by the service network, the access capability of the bearing network is the same as that of a traditional internet, and the bearing network can provide end-to-end credibility, safety, certainty and other capabilities for the service network. The information communication network can support services (namely OTT services) which can be supported by the traditional internet and are completely decoupled by a service network and a bearer network, and can also support services (namely non-OTT services) which cannot be supported by the traditional internet and have collaborative management and control requirements on the service network and the bearer network. A traditional internet is a network which takes a service network as the center and takes a bearing network as the best effort for transmission. The information communication network is centered on the bearer network, and the service network can obtain support of credibility, security, certainty and other capabilities provided by the bearer network.

The invention discloses a vehicle fault positioning and rescue scheduling platform and method based on GPS track acquisition, and relates to the field of vehicle fault positioning and rescue scheduling, and the method comprises the steps: uploading vehicle GPS and OBD data obtained by a vehicle-mounted terminal to a cloud database through employing a mobile network technology; according to the vehicle GPS and OBD data in the cloud database, the vehicle fault type is predicted and judged, and the position coordinate information of the vehicle fault is obtained; based on an expert scoring method, processing response levels of different vehicle fault types are set; accessing a high-precision digital map API (Application Program Interface), and marking schedulable rescue points and fixed GPS (Global Positioning System) coordinate data in a digital map; and establishing a vehicle fault rescue scheduling model, and carrying out comprehensive evaluation and scheduling on surrounding schedulable rescue points. The method has the advantages of effectively predicting the state and fault of the vehicle, efficiently responding to the processing of the fault of the vehicle, and preferentially scheduling and screening the schedulable rescue points.

The invention discloses a method and equipment for downloading a large file of a browser, belongs to the technical field of front-end Web, and is used for solving the problems that a large file downloading scheme in a traditional browser environment is low in memory use efficiency and data storage efficiency, insufficient in network interruption processing capability and lack of effective integration of a browser underlying technology, and the network interruption processing capability is poor. And offline caching and high-efficiency recovery are difficult to realize. The method comprises the steps of fragmenting a target file in a file downloading task according to a preset fragmentation rule, and creating a fragmentation downloading task for each fragmentation; dynamically distributing fragmented downloading tasks through a parallel task pool; after each fragment downloading task is completed, the downloaded fragment data and metadata thereof are stored through an IndexedDB database, and integrity verification of the fragment data is carried out; according to a fragment integrity verification result, re-downloading the current fragment; and after all fragment downloading tasks are executed, reading all fragment data according to a fragment sequence and splicing the fragment data into a complete file.

Embodiments of the present application provide a method resource sharing between network technologies. The method includes: receiving indication information, where the indication information indicates a control resource set among multiple control resource sets, the control resource set comprises time-frequency resources for first downlink control information (DCI), the multiple control resource sets include a first control resource set, and the first control resource set includes part or all of a second control resource set associated with a second radio access technology; and receiving the first DCI based on the indication information. Part or all of the first control resource set could be shared between the first radio access technology and the second radio access technology, and the resource utilization could be improved.

The invention relates to the technical field of communication networks, in particular to a dynamic resource allocation method and system for an industrial 5G network. The method comprises the following steps: acquiring industrial terminal data; identifying network congestion nodes based on industrial terminal data; detecting a link congestion fault according to the network congestion node, and determining a bandwidth utilization rate; allocating spectrum resources based on the bandwidth utilization rate; performing frequency spectrum slicing on the frequency spectrum resources, and determining a flow load amount; adjusting load balance according to the traffic load, and generating a load resource report; dynamically adjusting time slot allocation by using a load resource report, and determining a task priority; task scheduling simulation is executed according to the task priority, and if congestion occurs, a congestion snapshot is stored; reducing scheduling conflicts according to the congestion snapshots, releasing bandwidth resources, and determining available network slice capacity; power resources are dynamically adjusted based on available network slice capacity. The bandwidth utilization rate and the task scheduling efficiency of the industrial 5G network are improved based on the communication network technology.

The invention discloses a complex environment information acquisition method based on a sensor network, and relates to the technical field of sensor networks, and the method comprises the steps: building a complex environment portrait and a service level, and generating a scene configuration comprising an information freshness budget, a risk heat map and an alarm threshold; based on scene configuration, configuring mesh wireless networking, time synchronization parameters and candidate relays, and constructing observation configuration and quality rules; and generating a data stream and a real-time queue state according to a self-adaptive sampling strategy and an event mark, calculating an emergency degree and distributing a forwarding probability according to an information freshness budget by combining a candidate relay and a risk heat map, outputting uploaded data and a quality record, completing convergence and grading alarm according to an alarm threshold, and generating a disposal feedback and audit index. The state set, the uncertainty set, the link idle degree and the energy sufficiency are linked under the constraint of the information freshness budget, so that hot spot encryption, low-risk sparseness, reduction of energy consumption and conflict rate and standard observation time efficiency are realized.